Prof. Sharmila S P the research work focuses on advancing cybersecurity through AI-driven, explainable, and resilient detection mechanisms capable of addressing modern, highly obfuscated threats. Central contributions include the development of memory-forensic-based feature extraction techniques that enhance the transparency and interpretability of obfuscated malware detection models, enabling isolated family distinction and reducing false positives. The work explores multi-class classification frameworks for malware analysis, leveraging machine learning paradigms to identify sophisticated adversarial behaviors across diverse threat categories. Additional research investigates Hidden Markov Model–based intrusion detection, employing a randomized Viterbi algorithm to strengthen anomaly recognition in dynamic network environments. Studies on cyber-attack prediction further analyze prevalent forecasting techniques to improve proactive defense capabilities. Complementary research examines Android malware behavior, distributed ledger applications for secure banking operations, and lightweight authentication mechanisms rooted in keystroke dynamics for user verification. With a strong emphasis on AI, machine learning, GNNs, NLP-driven analysis, reverse engineering, and volatile memory forensics, the overall body of work contributes toward building robust, explainable, and scalable cybersecurity systems capable of safeguarding digital infrastructures against evolving threats in cloud environments, embedded systems, mobile platforms, and large-scale networked ecosystems.